WiFiUS: Collaborative Research: Data-Guided Resource Management for Dense Heterogeneous Networks

WiFiUS：协作研究：密集异构网络的数据引导资源管理

• 批准号: 1457060
• 负责人: Xin Liu
• 金额: $ 19.33万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1457060&HistoricalAwards=false
• 关键词: WiFiUS; Collaborative; Research; Data; Guided

The emerging paradigm of dense heterogeneous wireless networks (HetNet), while essential to meeting the explosive growth of wireless traffic, poses unseen challenges to classical cellular network design principles. In traditional cellular systems, spectrum and resource management are often based on the assumption of static and regular topology/traffic patterns. Further, each cell operates with an orthogonal set of resources and requires little inter-cell coordination. However, such traditional approaches are out-dated and inefficient in dense HetNets:the proliferation of small-size cells makes the network topology and traffic characteristics highly irregular and varying; and tighter coordination across cells becomes a necessity when users traverse many small-cells frequently. Thus, there is an acute need to re-examine fundamental ways to design HetNets that can (i) quickly adapt to irregular topology and changing load patterns, and (ii) manage cell coordination at scale with minimal overhead to achieve robust and dependable application-level performance. To address this open challenge, this WiFiUS project develops new framework, architecture and algorithms for adaptive, efficient, and dependable spectrum management and resource allocation in dense heterogeneous cellular networks. First, the project team develops a new hypercell architecture that views a macro-cell and the overlapping small-cells as a single logical entity. This new architecture allows hypercells to acquire a global view of the load/channel/mobility/application patterns across multiple base-cells, thereby enabling more effective and dynamic multi-cell coordination at scale. Furthermore, the team develops a data-guided operational framework that exploits both provider-collected and user-contributed data to address the inherent complexity of dynamic multi-cell coordination. Based on historical data, this approach migrates higher-complexity computations to offline, and thus achieves high spectrum efficiency and user QoE with low-overhead. The US/Finland team brings together a wide range of wireless expertise from physical layer design to network resource management. Having a successful collaboration record, the team carries out joint research activities in the following three aspects: 1) data collection and traffic pattern identification; 2) data-guided spectrum management and base-station cooperation; and 3) hypercell link adaptation, measurement, and resource management. The success of the project has a broad impact on the wireless communications industry by addressing multiple timely and critical challenges that arise from the exponential growth of cellular traffic. The project outcomes will also provide important insights into big-data mining and learning for cellular resource management. The results from this work will be widely disseminated through journal/conference publications, and be incorporated into undergraduate and graduate education endeavors.

密集异构无线网络（HetNet）的新兴范例虽然对于满足无线流量的爆炸性增长至关重要，但对经典蜂窝网络设计原则提出了前所未有的挑战。在传统蜂窝系统中，频谱和资源管理通常基于静态和规则拓扑/业务模式的假设。此外，每个小区利用正交的资源集进行操作，并且几乎不需要小区间协调。然而，这样的传统方法在密集的HetNet中是过时的和低效的：小尺寸小区的扩散使得网络拓扑和业务特性高度不规则和变化;并且当用户频繁地遍历许多小小区时，跨小区的更紧密的协调变得必要。因此，迫切需要重新审视设计HetNet的基本方法，这些方法可以（i）快速适应不规则拓扑和不断变化的负载模式，以及（ii）以最小的开销管理大规模的小区协调，以实现鲁棒和可靠的应用级性能。为了应对这一挑战，WiFiUS项目开发了新的框架，架构和算法，用于密集异构蜂窝网络中的自适应，高效和可靠的频谱管理和资源分配。首先，项目团队开发了一种新的超单元架构，将宏单元和重叠的小单元视为单个逻辑实体。这种新的架构允许超级小区获得跨多个基本小区的负载/信道/移动性/应用模式的全局视图，从而实现更有效和动态的多小区协调。此外，该团队还开发了一个数据指导的操作框架，该框架利用提供者收集的数据和用户贡献的数据来解决动态多细胞协调的固有复杂性。基于历史数据，该方法将较高复杂度的计算迁移到离线，从而以低开销实现高频谱效率和用户QoE。美国/芬兰团队汇集了从物理层设计到网络资源管理的广泛无线专业知识。该团队有着成功的合作记录，在以下三个方面开展联合研究活动：1）数据收集和流量模式识别; 2）数据引导的频谱管理和基站合作; 3）超小区链路自适应，测量和资源管理。该项目的成功对无线通信行业产生了广泛的影响，解决了蜂窝流量指数增长带来的多个及时和关键的挑战。项目成果还将为大数据挖掘和蜂窝资源管理学习提供重要见解。这项工作的结果将通过期刊/会议出版物广泛传播，并纳入本科生和研究生教育的努力。